Uncovering the causal relationships in plant-microbe ecosystems: A time series analysis of the duckweed cultivation system for biomass production and wastewater treatment.

IF 8.2 1区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Science of the Total Environment Pub Date : 2024-12-20 Epub Date: 2024-11-29 DOI:10.1016/j.scitotenv.2024.177717
Hidehiro Ishizawa, Yosuke Tashiro, Takashi Okada, Daisuke Inoue, Michihiko Ike, Hiroyuki Futamata
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Abstract

The complex interplay among plants, microbes, and the environment strongly affects productivity of vegetation ecosystems; however, determining causal relationships among various factors in these systems remains challenging. To address this issue, this study aimed to evaluate the potential of a data analytical framework called empirical dynamic modeling, which identifies causal links and directions solely from time series data. By cultivating duckweed, a promising aquatic plant for biomass production and wastewater treatment, we obtained a 63-day time series data of plant productivity, microbial community structure, wastewater treatment performance, and environmental factors. We confirmed that empirical dynamic modeling can identify the correct causal directions among temperature, light intensity and plant growth, solely from time series data. Extending the analysis to microbial community data suggested that the bacterial family Comamonadaceae positively affects host duckweed growth and nitrogen removal. Additionally, the predicted abundance of bacterial genes relevant to xenobiotics biodegradation was shown to have a positive effect on organic pollutant removal, supporting the significant role of bacterial metabolism in phytoremediation performance. These results demonstrate the effectiveness of empirical dynamic modeling in uncovering causal relationships within vegetation ecosystems, which are difficult to examine comprehensively through conventional experiment-based approaches.

揭示植物-微生物生态系统的因果关系:用于生物质生产和废水处理的浮萍栽培系统的时间序列分析。
植物、微生物和环境之间复杂的相互作用强烈影响植被生态系统的生产力;然而,确定这些系统中各种因素之间的因果关系仍然具有挑战性。为了解决这一问题,本研究旨在评估一种称为经验动态建模的数据分析框架的潜力,该框架仅从时间序列数据中识别因果关系和方向。通过培养具有生物质生产和废水处理潜力的水生植物浮萍,我们获得了植物生产力、微生物群落结构、废水处理性能和环境因素的63 d时间序列数据。我们证实,仅从时间序列数据中,经验动态模型可以正确地识别温度、光照强度和植物生长之间的因果关系。将分析扩展到微生物群落数据表明,Comamonadaceae细菌家族积极影响寄主浮萍的生长和氮的去除。此外,预测的与外源生物降解相关的细菌基因丰度被证明对有机污染物的去除具有积极作用,支持细菌代谢在植物修复性能中的重要作用。这些结果证明了经验动态建模在揭示植被生态系统内部因果关系方面的有效性,而传统的基于实验的方法难以全面研究这些因果关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Science of the Total Environment
Science of the Total Environment 环境科学-环境科学
CiteScore
17.60
自引率
10.20%
发文量
8726
审稿时长
2.4 months
期刊介绍: The Science of the Total Environment is an international journal dedicated to scientific research on the environment and its interaction with humanity. It covers a wide range of disciplines and seeks to publish innovative, hypothesis-driven, and impactful research that explores the entire environment, including the atmosphere, lithosphere, hydrosphere, biosphere, and anthroposphere. The journal's updated Aims & Scope emphasizes the importance of interdisciplinary environmental research with broad impact. Priority is given to studies that advance fundamental understanding and explore the interconnectedness of multiple environmental spheres. Field studies are preferred, while laboratory experiments must demonstrate significant methodological advancements or mechanistic insights with direct relevance to the environment.
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